Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China
Elevated O<sub>3</sub> concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses on investigating the key factors affecting O<sub>3</sub> formation in the ecologica...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/4/457 |
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| author | Yan Liu Tingting Hu Yusen Duan Jingqi Deng |
| author_facet | Yan Liu Tingting Hu Yusen Duan Jingqi Deng |
| author_sort | Yan Liu |
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| description | Elevated O<sub>3</sub> concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses on investigating the key factors affecting O<sub>3</sub> formation in the ecologically sensitive Dongtan Wetland (Chongming District, Shanghai, China) area. By comparing the performance of O<sub>3</sub> concentration prediction of multiple machine learning models, this study found that the random forest model achieved the highest accuracy (R<sup>2</sup> = 0.9, RMSE = 11.5). Feature importance and structure mining showed that peroxyacetyl nitrate (PAN), nitrogen oxides (NOx), temperature, wind direction, and relative humidity were the main drivers of O<sub>3</sub> formation. Specifically, PAN concentrations exceeding 0.1 ppb and temperatures above 3 °C were found to have a significant impact on O<sub>3</sub> levels, especially in spring, summer, and autumn. Trajectory analysis showed that westward urban pollution and emissions transported from the ocean were the main factors in O<sub>3</sub> formation in the area. This study highlights the need for targeted emission control strategies, especially for PAN precursors generated by ships and NOx generated by urban industries, providing important insights for improving air quality in ecologically sensitive coastal areas. |
| format | Article |
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| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
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| series | Atmosphere |
| spelling | doaj-art-d945f550deac41ffa082958d5b5d2e512025-08-20T02:28:27ZengMDPI AGAtmosphere2073-44332025-04-0116445710.3390/atmos16040457Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, ChinaYan Liu0Tingting Hu1Yusen Duan2Jingqi Deng3College of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaCollege of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaShanghai Technology Center for Reduction of Pollution and Carbon Emission, Shanghai 200235, ChinaCollege of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaElevated O<sub>3</sub> concentrations pose a significant threat to human health and ecosystems, but little research has been performed on coastal wetlands near large cities. This study focuses on investigating the key factors affecting O<sub>3</sub> formation in the ecologically sensitive Dongtan Wetland (Chongming District, Shanghai, China) area. By comparing the performance of O<sub>3</sub> concentration prediction of multiple machine learning models, this study found that the random forest model achieved the highest accuracy (R<sup>2</sup> = 0.9, RMSE = 11.5). Feature importance and structure mining showed that peroxyacetyl nitrate (PAN), nitrogen oxides (NOx), temperature, wind direction, and relative humidity were the main drivers of O<sub>3</sub> formation. Specifically, PAN concentrations exceeding 0.1 ppb and temperatures above 3 °C were found to have a significant impact on O<sub>3</sub> levels, especially in spring, summer, and autumn. Trajectory analysis showed that westward urban pollution and emissions transported from the ocean were the main factors in O<sub>3</sub> formation in the area. This study highlights the need for targeted emission control strategies, especially for PAN precursors generated by ships and NOx generated by urban industries, providing important insights for improving air quality in ecologically sensitive coastal areas.https://www.mdpi.com/2073-4433/16/4/457O<sub>3</sub> formationstructure miningperoxyacetyl nitratemachine learning |
| spellingShingle | Yan Liu Tingting Hu Yusen Duan Jingqi Deng Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China Atmosphere O<sub>3</sub> formation structure mining peroxyacetyl nitrate machine learning |
| title | Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China |
| title_full | Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China |
| title_fullStr | Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China |
| title_full_unstemmed | Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China |
| title_short | Predicting Ozone Concentrations in Ecologically Sensitive Coastal Zones Through Structure Mining and Machine Learning: A Case Study of Chongming Island, China |
| title_sort | predicting ozone concentrations in ecologically sensitive coastal zones through structure mining and machine learning a case study of chongming island china |
| topic | O<sub>3</sub> formation structure mining peroxyacetyl nitrate machine learning |
| url | https://www.mdpi.com/2073-4433/16/4/457 |
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